Ad Decision Server Personalization via Content Metadata Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current personalized media delivery systems, such as those for adaptive streaming, fail to effectively match advertisements with the actual content being streamed, leading to inefficiencies in network traffic and resource consumption due to the lack of consideration for content metadata in ad decision-making processes.
Innovation Solution
A method that utilizes a computer-implemented matching mechanism to match messages with video content, incorporating relevance algorithms that calculate a dynamic relevance score based on content metadata, demographic constraints, and operational costs to optimize the selection and delivery of personalized messages, thereby reducing network traffic and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ad decision servers select personalized ads independently without considering content metadata, then ad delivery can be performed quickly, but the relevance between ads and video content is insufficient
Solution Approach 1:
The patent combines the ad decision-making process with the video delivery process by integrating content metadata analysis into the ad selection mechanism. The ad decision server now considers video content metadata (genres, keywords, descriptions) alongside user profile information to make ad selections, merging previously separate decision factors into a unified relevance assessment system.
Solution Approach 2:
The patent introduces a relevance calculation mechanism that acts as an intermediary between content metadata and ad selection. This intermediary component processes video content metadata, extracts meaningful features, and uses them to evaluate ad relevance scores, thereby bridging the gap between content characteristics and ad matching without requiring complete system redesign.
2Measurement precision
If more data segments are provided to improve personalization accuracy, then ad relevance improves, but network traffic and resource consumption increase
Solution Approach 1:
The patent extracts only the essential and most relevant features from video content metadata (such as key genres, primary keywords, and critical description elements) rather than processing entire video files or all metadata. This selective extraction approach maintains personalization accuracy while significantly reducing the data volume that needs to be transmitted and processed.
Solution Approach 2:
The patent applies partial action by providing a subset of the most relevant ad candidates rather than evaluating and transmitting all possible ads. The system calculates relevance scores and selects only the top-matching ads, performing sufficient analysis to ensure quality personalization without the excessive resource consumption of comprehensive evaluation.
3Adaptability or versatility
If ad decision making is performed independently from video delivery, then system simplicity is maintained, but content-based personalization is insufficient
Solution Approach 1:
The patent performs preliminary analysis of video content metadata before ad selection is made. By pre-processing and extracting relevant features from video content (such as identifying key genres, themes, and descriptive elements), the system prepares the necessary information in advance, enabling content-based personalization without adding complexity to the real-time ad decision-making process.
Data Source
AI summary
The present disclosure relates to a method and an ad decision sever for determining personalized messages during a provisioning of digital content. The method comprises to provide a set of personalized messages candidates by executing a relevance algorithm, which matches message metadata and content metadata. The method continues with receiving calculated network costs for the set of personalized message candidates in order to determine a set of personalized messages, which are optimized with respect to the received network costs by applying a multivariate optimization algorithm.